Python's experimental JIT to be removed
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On one hand, it seemed like JIT work was really progressing in high gear until SC requested a new PEP. Also unfortunate, as it seemed like part of the hope for the JIT was that it'd counter the performance penalty from free threading.
On the other, it did feel (to me) like with all the fancy (and definitely cool) optimizations it got (automatic refcount operation removal, new type-based specializations, inplace math operations), the macrobenchmark benefits weren't _that_ exciting for a JIT. It definitely has potential for more, maybe with even fancier optimizations like unboxing ints, but the complexity just keeps going up.
> It said that the PEP was "overly optimistic" about user-visible differences with the JIT enabled.
The JIT is technically generated from the same source as the interpreter loop itself, so originally the differences were extremely unlikely, but I imagine the risk just kept going up as new JIT-only paths and optimizations were introduced.
And politically, I feel like SC’s reasoning was just “this side project has landed in main branch almost by accident and has been stewing without oversight ever since, with no stabilization in sight”; and the latest PEP was probably still too vague, with a lot of “here’s more things we can add to the stew” - so I can understand if SC wasn’t satisfied.
JITs are big nasty things that by their nature have to do a lot of things. You are basically saying "Give us a PEP for a new python interpreter and compiler".
What they do is fuzzy by their nature because optimizations are very rarely universally beneficial. It's a game of measuring, looking for regressions, and tackling the biggest problem next for whatever sample applications you have available. You always run into the risk of overfitting on top of all that (You may have made django faster, but you've made flask slower).
This is the sort of thing that, IMO, should not go through the PEP process. It's an implementation detail for the runtime.
Imagine, for example, if the JVM needed a bunch of JEPs for JIT improvements. They'd never get done. Or if any javascript engine needed ECMA acceptance before they could do a new JIT optimization.
FWIW, I definitely can find a bunch of both PEPs and JEPs for internal optimizations, random examples:
1. An outsider to the regular OpenJDK dev team proposing an optimization.
2. Advertising for a cool new performance tweak with potentially large impact.
You'll also notice that the JEP descriptions are very different from PEP descriptions.
Probably the largest JEP for the JVM, Valhalla
Does not have the same level of requirements being asked of the Python JIT. Further, it's well understood that this JEP will "live on main" for quite some time in preview before it's accepted and merged.
What the JEP does, instead, is simply describe the feature and potential risks.
The vast majority of JVM optimizations do not get a JEP.
> The vast majority of JVM optimizations do not get a JEP.
In Python too. Still, the JIT really is just a really big thing. I don’t know if there could be a better way to do it, as either 1. You develop it off-branch and… make an enormous PR when you’re done, with risks of getting rejected anyway? Or 2, you allow more experiments on main like Rust, but then you end up with hundreds of minor half-maintained nightly-only features, possibly conflicting with each other, and eventually go through bureaucracy to remove them anyway.
The Java governance allows for a lot more open ended projects with follow ups. Not that it's reckless, Valhalla is proof of that, but rather that once something is good enough they are pretty open to just shipping it as a preview feature and refining until it is worthy of being stable.
That's why, for example, we saw ZGC get merged, and then JDK after JDK seeing continuing development and improvement on ZGC (Or G1 for that matter). These GCs were fairly rough on their first release and now they are pretty much best in class.
I see a Python JIT as the same thing. Delivering the infrastructure for a JIT would need a PIP, but I wouldn't put in things like "acceptance criteria" beyond "doesn't break older code". I would expect such a project to get continual updates without PEPs.
Just for completeness: the original JIT PR was merged quite enthusiastically and only after the fact it was pointed out that it should have went through a PEP process [1]. The following PEP 744 was of the “informational” type, and the just-withdrawn PEP 836 was the first “standards track” PEP, which means that (in my understanding) it’s technically the first actual PEP that the SC would formally comment on. So in a way it’s not “can we update the JIT” PEP, but the first real “does SC want this JIT as proposed” PEP.
That said, I do agree with most of what you said, especially the acceptance criteria part.
The issue I have and find it hard to resolve is that since the code was already on main, the feature was already in “sunk cost” mode. It’s hard when the code is already on main and another 3 optimization PRs are always there behind the corner, but it needs to be possible for SC to say “we decided it’s not actually worth all this complexity for <some small number>% speed improvement” (I’m not commenting whether in this particular case it’d have been a right call), and the PEP (if not withdrawn) was the way to exercise that control.
[1] https://github.com/python/cpython/pull/113465#issuecomment-1...
And even then, Hotspot did not got a JSR, because all that work was done before Sun started collaborating with industry partners for Java evolution.
They "simply" ported the JIT compiler from the Strongtalk acquisition into Java.
They do:
Not every optimization requires a JEP, but certainly something as big as "add or remove a JIT" would 100% require a JEP.
Anyways. I don't believe that Python needs a JIT compiler. I don't believe that CPython can realistically replace its interpreter with JIT compiler without causing major problems with the community packages.
The way I see it: Python users today desperately want Java. They want it called Python because there's some vague memory still around of Python being cool and Java not being cool.
In Russian there's a children tale about a hungry soldier returning from a war and a greedy old woman who offered him a bed for the night. The old woman then pretends to have no food for dinner, and a soldier promises her a magic trick of making a porridge out of a hatchet. And so he tells the woman to put a pot on a stove with a hatchet in it, and step by step he makes her add all the ingredients necessary to make a porridge to the pot. The story gave birth to the idiom "hatchet porridge", which is used to describe anything that was created in a similar way: by deception, when another goal was allegedly being pursued.
So, to me, the current state of Python is this "hatchet porridge". None of its users want the actual Python (the hatchet is still sticking out of the pot), and they keep adding Java ingredients to the pot in hopes to cook up Java. And so was JIT. It just turned out to be too difficult to throw away the hatchet. An the porridge still has some chipped wood in it as well as a metallic taste.
Dunno how much investment is needed into language optimization anymore.
Conversion is now (somewhat) easier, the testing will be the slow part. For the speed improvements, which we also need for a migration from on-prem to cloud, it's a no brainer vs the other options at our disposal (like GraalPy).
Because having just the hot loop in the performant language is often deemed good enough. And there's tons of Python code out there where the "hot loop" is already automatically in C (the various operations implemented in C by the runtime) or where you can get close enough e.g. with Numpy. Consider that things like Pygame can exist; for a lot of the games people want to make, the only thing that really needs to happen anywhere near "bare-metal" speed is blitting.
It was done plenty. Just with extensions to python like numpy and opencv. There is no reason to translate everything versus just the critical pieces.
A fairly massive undertaking for existing code, which jit was supposed to be useful for.
Which honestly, is the right approach.
Zero chance of shoehorning that into a standards-compliant Python runtime. Some things are too fundamental to change without affecting the user-visible language: JITting, lazy imports, threading, copying gc, ref counting, etc.
This way I can easily write inlined functions with proper C speed, in a Python esque language.
Reasons why I rarely use Cython without this:
- unmodified Python code running in Cython is often faster, but not that much. 50% or so is my rule of thumb.
- but the development style is very different for Cython and Python, adding a lot of impedance. Eg reloading doesn’t quite work, profiling is different, you need a build directory and a build step at deployment etc.
- Cython proper is different enough and I’d argue unintuitive enough that if I have to have all these things in place, I might as well wrap my function in Rust.
Such a decorator would give you a low friction way to make things faster when you need it, with scope for making it much faster via all the funny Cython, while keeping Python-esque semantics, in particular "development semantics".
I think it would require some language modification since Cython was definitely a superset of Python; I think some of that syntax has been streamlined, but maybe still a strict superset.
https://github.com/py2many/py2many
https://github.com/adsharma/fquery/blob/main/tests/django_example_model.pyFolks, numba is great. To first approximation: write your python function to "look like C"[1] and work over the numpy arrays you probably already have and understand, put a "@jit" decorator on it, and you're done. It JITs and runs your code natively at native performance.
With some extra effort, there are features for SIMD vectorization and thread parallelism, and even GPU backends (that, full disclosure, I've been pretty frustrated by -- but they do work).
[1] Which is to say, don't touch Python interpreter data structures. No dicts or objects or lists or strings, etc... If you're bound by the interpreter[2] you're bound by the interpreter and nothing is going to help you.
[2] A corrolary here is that the kind of C code numba is NOT good for is "following pointers between structs", which is something that can't be expressed in either python or numpy.
I don’t know how people who’ve worked with Python and been frustrated with its performance don’t know about Numba.
The one aesthetic gripe I have is that the LLVM included is a fork. It won't even try to work with the system LLVM and insists on installing a huge wheel with custom binaries. But on a modern system an extra LLVM is noise, so I guess I don't care that much.
It's not technically a fork, it's a standalone build of vanilla LLVM (ok, there are a couple patches: https://github.com/numba/llvmlite/tree/main/conda-recipes).
> It won't even try to work with the system LLVM and insists on installing a huge wheel with custom binaries.
Not only does LLVM not have a stable ABI, it doesn't even have a stable API. It's difficult to write code that works reliably against a non-trivial number of LLVM major versions.
For the record, here is the LLVM compatibility table for llvmlite versions (llvmlite is the Python-C++ shim that Numba uses to talk to LLVM): https://llvmlite.readthedocs.io/en/latest/#llvm-compatibilit...
If you want a Numba linked against your system LLVM, the solution would be for your distribution to provide a system package for Numba (and llvmlite).
That's a really intense restriction. Sounds like the name fits; it's not general purpose, it's jit for number crunching and little else.
I'm not sure exactly what you mean by "bound by the interpreter" but there's nothing about touching those data structures that requires the interpreter to be involved in most cases. The problem of slow APIs can be solved by the JIT having intimate knowledge of python's internals, can't it?
Is it? An object field access in python is a dynamic hash lookup followed by a method call as a fallback (itself involving hashed lookups up the class hierarchy). That's not an "interpreter vs. native" kind of thing, that's just the semantics of the language that have to be preserved. And that's just not going to be "fast" in the way that the user of a JIT would want. Code dominated by Python datastructures is going to be "slow" in ways that JITs can't fix, ever.
Which is why Python appears to have given up on the effort in this space. PyPy too[1] really never delivered on the desired performance gains (like 5x or so on typical code) without forcing the user into careful optimization.
Numba, to its credit, doesn't even try. It falls back to python if it must for python stuff, but is quite clear that it's providing no benefit if that's what you're doing. And the stuff it does do is obvious and clean and extremely useful.
[1] PyPy is also looking pretty moribund recently.
Maybe, but the world is still waiting for the counterexample. Python is simply not a runtime well-suited to optimization.
In the meantime, those of us who actually enjoy real HPC problems are using numba and rolling our eyes about all the confuffery around JITs. Maybe one will arrive that proves us wrong, but numba is fast today.
I don't have enough experience with PyPy to say much about it.
I wish Lua 5.1 could become part of POSIX; there are a lot of implementations of it out there.
The upthread comment was about an idea for "inline Cython", which was then hijacked because that's largely what numba is. These are technologies aimed at optimized native code running over native types to do stuff like numerics and ML and other HPC tasks. Lua isn't any more responsive to this domain than Javascript has been, it's not really the subject under discussion.
When OP wrote "inline Cython" they didn't seem focused on numerics, they talked about "proper C speed", and they mentioned modifying the language. I don't think it's getting off-topic to suggest a deeper integration that lets the JIT be very intimate with the interpreter and truly inline things in a way that bypasses language-crossing and type-dispatching overhead. As long as you're in the hot path, let it look up object fields in a couple instructions.
Isn't that basically what I said? Lua isn't "proper C speed", nor is PyPy, the cancelled Python JIT in question, nor even V8. That's a different problem area with different technologies. The fact that you like coding backend stuff in Lua or whatever isn't really saying much about whether numba is great.
But it is. Numba is great.
Numba is great at its niche. But OP directly said Numba is "not really" what they were looking for because it's so narrow.
Lua5.1 is much more stable by the virtue that the only updates it code gets are very minor changes to fix any lingering security issues it has (the last Lua5.1 CVE is from 2014).
[1] I guess these days a LLM can do the grunt work of converting a Python2 script to Python3.
[2] There is a project which converts Lua in to native code: https://github.com/samyeyo/clx
Lua 5.1’s edge over Javascript and Python is that it’s a lot smaller and simpler.
[1] It’s possible to recompile Lua 5.1 to have the numeric data type be an int, but the standard compiles of Lua5.1 only have double precision floats for numbers. Bitwise operators are technically not part of Lua 5.1, but a library to add support is easy and is usually done: Most Lua5.1 forks including LuaJIT and MediaWiki’s version of Lua5.1 have such a library available. Later versions of Lua have both int and float support.
But that doesn't help much if/when you're doing two hash lookups per iteration just to find two small integers to add together.
Can you not mostly cache whether anything weird needs to happen and do the direct access with minimal overhead?
You said elsewhere "But that doesn't help much if/when you're doing two hash lookups per iteration just to find two small integers to add together." but looking up two fields isn't inherently a very big overhead. And you can cache the actual hash, and if you want to you can make sure the fields on similar objects have a consistent offset and cache that offset to make it even faster. This code using lots of objects is presumably not the core number crunching on arrays; we're doing more logic per access and we can handle a few instructions to get a value.
Maybe also then, importing module as Cython?
import django with Cython
On another note, I'm working on a (big) PEP for python, and looking for maintainers and sponsors (as per python's rules). Do someone know where I could find such amazing persons? (I already have the code, works in cython,graalVM and pypy, and it's kind of a big change but I believe it will help a lot the language)
A pain point that is big enough that at least two programming languages, Julia and Mojo, were created to move beyond it.
Which would not even be an issue, if Lisp, Scheme and Prolog had not lost their aura among AI devs.
Mojo in particular recognized to compete with Python they'd have to be a superset, then they backed of that goal when they realized it would be too hard.
So at the end of the day, Python has a value proposition and it cannot be entirely subsumed by other languages, because those languages make tradeoffs that move away from Python's value.
Python has a known set of trade-offs, mostly related to loop performance. But if you understand the trade-offs and available mitigations, it should not stop you from building good software. You know, just like any other ecosystem.
[EDIT - I assume you are commenting on Dartmouth BASIC.]
Since we have to prove everything on Internet forums,
https://archive.org/details/bitsavers_dartmouthB_3679804/mod...
=> Saving of Compiled Programs
Python already compiles to bytecode (the .pyc files are a cache thereof). It's just that interpreting even the simple bytecode instructions can take dozens of clock cycles each. I imagine that a lot of this is not in the interpreter loop (which has seen quite a bit of optimization, IIRC up to the level of "predictive" gotos within a big switch statement within a loop) but just unwrapping objects and moving values to and from registers, dealing with the fact that there is basically never any cache coherency for anything etc.
Basically I'm not convinced that writing out the set of machine-code instructions needed to interpret the bytecodes would be a big win, because it takes too many instructions for simple tasks.
Objective-Smalltalk runs on the Objective-C runtime, so Objective-Smalltalk methods look like Objective-C methods accessed via objc_msgSend() to the entire rest of the system, including to other Objective-Smalltalk code.
That means it doesn't really matter to the rest of the system if any particular method is interpreted, AOT-compiled or JIT-compiled. They can't tell, except for execution speed.
And of course the execution model includes machine primitives, which the interpreter also deals with but doesn't make faster, but the native compilers do.
There are times that it generates faster code than Cython! Of course with Cython you can tweak it and push its limits with various optimizations and other techniques, but for using your existing code as it is and gain massive performance improvements, mypyc it's a must!
Code sample:
import time
def fib(n: int) -> int:
if n <= 1:
return n
else:
return fib(n-2) + fib(n-1)
startTime = time.time()
fib(35)
duration = time.time() - startTime
msduration = duration * 1000.0
uduration = msduration * 1000.0
print(f'{duration:.6f} seconds, {msduration:.2f} ms, {uduration:.2f} μs')
$ python3 mypyfib.py
1.452477 seconds, 1452.48 ms, 1452477.22 μs
$ # the same code compiled with `mypyc mypyfib.py` produces the following:
$ python3 -c "import mypyfib"
0.063701 seconds, 63.70 ms, 63700.91 μs
95.61% reduction in time
2180.15% increase in performance
22.80x faster
Not to say that the technical objections were without merit, they likely very much weren't, and the debugger and instrumentation support and maintenance work sounds fairly painful on its own.
If having to put all that in writing and get a bunch of people to agree feels too burdensome to you, maybe that’s a sign that you would be happier working on something other than core changes to the world’s second most popular programming language.
(edit: That wasn’t meant to be a jab at the core developers working on the JIT. Based on what’s in the public discussion threads, they have been handling things very well throughout the process. It’s unfortunate that the SC let the project get this far before realizing the project had grown well beyond its original PIP’s mandate, and I’m sure that is deeply frustrating to the people leading the project. I know I would not be handling the setback nearly as gracefully as they are.)
The experiments had long been done. The topic at hand was including it as a core feature.
As they themselves pointed out, the problem isn’t that there’s anything wrong with an open-ended experiment. It’s just that open-ended experimentation doesn’t belong in main, of all places. They have an ongoing duty to ensure that a critical piece of global infrastructure remains stable and trustworthy to consider.
The scope of a JIT is the entire python runtime. Not at the beginning, it can be more narrow, but certainly it can evolve to cover everything. JITs touch everything.
> success criteria
Impossible to measure. JITs don't universally make things faster and unless the SC was willing to accept "some programs will see around x% improvement" then it would be impossible to actually give them the criteria they want. There's a reason C++ compilers and their optimizations aren't defined by the ISO committee.
> support plan
Like most JITs, support for a JIT is an ongoing and ever evolving process. What's needed depends entirely on what's needed today. You usually need more than 1 or 2 dev working on it, which may be the issue here.
> It’s just that open-ended experimentation doesn’t belong in main, of all places.
Very strong disagree. That's simply the nature of performance optimizations for JITs. They belong on main because new optimizations and heuristics will always be generated. The role of doing a compiler is a long term experiment in performance, seeing what your runtime is currently doing and how it's being used and tweaking it to work better.
pypy isn't in a worse place because it's been running a longterm performance experiment.
> They have an ongoing duty to ensure that a critical piece of global infrastructure remains stable and trustworthy to consider.
None of the things mentioned actually do that, other than perhaps a support plan. There are plenty of critical infrastructure projects who all can manage to develop JITs. Python is the special snowflake that has decided to do JIT development incorrectly.
This is a runtime optimization/implementation detail. It's foolish to send it through a committee approval process. That's why you won't be able to find a single language with a JIT or advanced optimizations that has do it by committee.
> This is a runtime optimization/implementation detail.
I view JITs as a critical architectural component. Like you said, it impacts the whole system. Optimization and implementation details don't usually do that.
It’s kind of wild how nice Python’s C extension system, which enables basically zero cost interop with native code, is for certain purposes. It supports a situation that’s comparable to the games industry’s tradition of putting a Lua or Lisp tip on top of a C++ iceberg.
Who is this a problem for?
I think there is enough evidence to conclude that most language ecosystems cannot support multiple implementations that are fit for general purpose usage. There are almost no successful examples, and there are many, many failures.
The most successful things I can think of:
C & C++ - often does require at least some pre-processor logic or different build system settings.
Java - I'm not 100% sure - I think there are credible implementations of both the JVM and compiler though of course almost everyone is building from the openjdk or the commercial fork.
Nodejs & Bun - Most of NPM just works.
...
Thats it. Honorable mentions like C#/Mono exist, but if you look at that ecosystem a majority of applications only ever built with one of those, not both.
There are niches maybe like some subsets of scheme or common lisp - but I think most apps have a single implementation creating the builds that they ship. There are re-implementations perhaps that would count that I don't know enough about like Delphi/Lazarus, but I truly don't think there is another mainstream example.
In Haskell, SPJ noted years ago that GHC was really the reference implementation and he no longer considered standards work to be worthwhile (though it has continued past that).
Others I think have reach the same conclusions, such as Ruby. I could be missing something but I don't think most platforms should even aspire to support this.
Perhaps these priorities were always irreconcilable.
Perhaps that’s why the existence of competing Python JIT projects that weren’t trying to commit directly into CPython’s main branch kept coming up in the conversation.
Bureaucracy doesn't definitionally mean Kafkaesque. It's popular to hate on it, but good or neutral bureaucracy does exist.
In this case a single committee directly engaged with both core developers and the community, through a fairly open process, explicitly bent several of their own official rules to suit the occasion, and indicated cases where further flexibility might be needed depending on how things develop.
I would call that a pretty stellar example of non-bureaucratic governance.
It’s true that that decision didn’t involve everybody getting their way. But that’s not organizational hell, that’s just organization. If you don’t want to contend with other people’s competing interests, the solution to that is solo development.
HN when an organization achieves its intended goals: this can't be bureaucracy because it's good.
In this case, it seems like the human bureaucracy made wise and measured decisions to contain the software bureaucracy. They made reasonable requests to clarify the scope and impact of a major, major core feature. The proposers didn't feel like that was doable, so a complexity nuke was avoided.
If all python had was __slots__ then we would not be struggling to JIT python.
Tight numerical loops is where the JIT adds performance.
Most performance sensitive features are already available in python between numpy, numba and Cython without really leaving the language core.
You'll mostly be speeding up the slow parts of python, not supercharging the fast parts.
I saw this presented as new tech at either OOPSLA or OSCon almost exactly 20 years ago.
CPU branch prediction is still pretty good even with the loss of function in recent years.
Heh, how about how `__class__` works? Not that I recommend actually doing this, but you can literally change the type of an already instantiated object by reassigning `__class__`:
>>> class Dog:
... def hi(self):
... print("woof")
...
>>> class Cat:
... def hi(self):
... print("meow")
...
>>> dog = Dog()
>>> dog.hi()
woof
>>> dog.__class__ = Cat
>>> dog.hi()
meow
>>> isinstance(dog, Dog)
False
>>> isinstance(dog, Cat)
True
>>> >>> Dog.__bases__ = (Cat,)
>>> isinstance(dog, Dog)
True
>>> isinstance(dog, Cat)
True
>>>See also the warning here:
https://developer.mozilla.org/en-US/docs/Web/JavaScript/Refe...
And this related article:
Modifying a variable that is read by another thread needs special care in all multithreaded languages I know. If you don't use a synchronization primitive to tell the other thread "hey, this thing here may have changed, read it again from memory"; the other thread is free to use its old value for as long as it whishes.
* Typing is a non-obligatory add-on to the syntax
* SQLAlchemy and other ORMs override basic operators like "=" and employ metaclass programming heavily to create Python-like SQL query builders
* Pytest rewrites all the assertions as it side-steps bytecode generation (so your .pyc files are different than what they would be without pytest)
* Many libraries depend on monkey-patching live library calls, including ones to external dependencies
* The way async was added in is another bolt-on where the same code behaves differently in different contexts
* Decorators can introduce any side-effect you imagine, and nobody (including core libraries) follow the golden rule of "don't do, only decorate"
* Many debugging and profiling mechanisms (including REPL) depend on these properties to function — including observability tooling like Sentry, Datadog etc.
...
Have you seen Ruby?
I don't know Python's innards well enough to understand why it's so especially difficult in this regard, but I gather around the edges that its object model may not be as well founded.
JavaScript also has monkey-patching, and it's also very common. E.g. SolidJS replaces objects with dependency-tracking proxies.
There are really no fundamental language reasons why Python should be more complex. From what I heard from a JIT developer, the main issue is not the code itself but the module system and code loading.
JavaScript was designed from the start to operate inside a sealed environment with only few external handles, with the code loaded upfront or added on-demand via "eval". Not so for Python, where native code modules can (and will) result in code loaded dynamically and it's hard to track what depends on what.
Yet both languages are on the genesis of modern JIT compilers, going beyond what was already being done for Lisps, Prolog, BASIC.
And the proxies make it slow as Python. Plain objects cannot suddenly become proxies, so the runtime can treat them as a special case.
All just as dynamic as Python, and with quite usable JIT compilers.
Or for that matter, PyPy and GraalPy do exist.
Can you just replace CPython with PyPy or GraalPy in every project? And why not? :)
PyPy and GraalPy then never get to use those pseudo "Python" libraries, because their API for C based extensions differs enough that you cannot take a random shared library and load them into either.
They came up with a common C API proposal, but has hardly taken that much adoption.
The python C API is the "killer app" of python, and what gave its edge against otherwise similar languages (ruby/js). That's makes it unique (the whole scientific stack). But a lot of early choices leaked into this API, and this (+ preference from guido for simple implementation) is at odds with doing JIT: lots of global state (instead of explicitly passing interpreter state), ref counting, no tagged pointers meaning boxing/unboxing everywhere, etc. Some of those are getting gradually fixed, but that's a slog, though maybe AI can accelerate a lot.
I had high hopes for HPy but that unfortunately fizzled out.
someone please think of the monkey patchers!
Not since before Ruby 1.9, no.
Wait, how many? I'd say just this one. The other that I know of, pypy, was massively successful for a single maintainer project. And definitely showed the possibility/potential of JIT python.
I wouldn't call it a failure for not over taking mainline CPython.
> Wait, how many? I'd say just this one.
I can name at least a couple others: Unladen Swallow, Pyston.
> The other that I know of, pypy, was massively successful for a single maintainer project.
PyPy had multiple active core developers during the 2000s.
There are already many way of addressing Python performance (leaving aside whether Python is a good choice if performance matters) such as Cython, Numba, Pypy and C extensions.
And all of them are lacking in different ways, we ALSO, want faster pure Python.
Pypy does provide faster pure Python. Its a compatible implementation with JIT.
And from what I understand there are limitations with using libraries that use the CPython API to interface with native code.
But the libraries limitations, and that it's not the "real" Python project, do get you.
PyPy wasn't great for me because its startup time interfered with the main usecase I wanted it for in one instance and in another there bugs that made it not quite compatible enough. Money could solve the bug issue.
On the other hand I did get pygame working with it at one point and that seemed like a nice scenario.
When it was created, PyPy did not seek to optimise startup performance. Instead, its aim was steady-state performance of JITed code, and it succeeded handsomely. A conceptually simple fix would be to optimise traces in one processor core, while the interpreter runs in another. Once when the trace is finalised, does interpretation stop and switch to native execution. But PyPy is currently a sequential system and making it parallel, while conceptually easy, is a lot of work and needs a rewrite of the whole system. But, for better or worse, the PyPy team does not have the resources to do this.
The other of PyPy's big performance issues (Python code using a lot of FFI calls via a suboptimal FFI) is also, in principle, easily fixable. But this is a social problem: the Python community would have to move towards better FFIs.
In principle, it would also be easy to fix PyPy's resource problem. I am not sure about the social problem ...
As we see over and over again in python core development. It's the worst.
A bit like Chrome there's a fully open source version and a proprietary version with a few extra bells and whistles, but the latter is entirely free for both open and commercial usage (you can't redistribute it for a fee). So you may as well use it.
It has other interesting features
• Scalable pauseless GC
• Single binary distribution of Python programs (like Go)
• Fancy and optimized interop with other languages including Java, JavaScript, Kotlin, Ruby and any other Truffle language.
• Supports native extensions. Tested and works with common libs like SciPy, NumPy, PyTorch, Pandas etc: https://graalpy.org/python-developers/compatibility/
• They're working on a GIL-free mode
In recent weeks there has been a release of a new web framework for Python that only runs on GraalPy, called Pyronaut (https://pyronaut.io/). It takes advantage of JVM interop to expose the Micronaut framework, which is similar to Spring for those who know it. Thanks to the JIT and well optimized base libraries it runs extremely fast, being about 3x faster than FastAPI+Granian, or 7x faster than Flask+Gunicorn and having many more features.
https://www.youtube.com/watch?v=zo6L7bxn4OY
(that was presented at Devoxx but the Pyronaut devs will start touring Python specific conferences soon)
So although CPython may not have made it past the JIT milestone, that doesn't mean Python as a language or community has to fail it. It does require openness to alternative implementations, but historically Pythonistas have been fairly good about that (e.g. Pypy was embraced back when it was more active).
This stuff does require you to accept that it's made by Oracle, but as it's open source, that should be OK.
Disclosure: I sit next to some of the people who work on this stuff.
How long does that take?
https://pyronaut.io/performance/
> Latency lines come from the load ramp (+25% per 15-second step); throughput from 45-second validation steps (+2% each) after a 180-second warm-up. Each framework ran twice and we show its best run. No profilers were attached.
So that's a "realistic" web serving benchmark if you're willing to grant the use of a GraalPy-optimized framework rather than Flask/FastAPI.
Warmup times for other benchmarks are currently quite unstable because there's a big focus on interpreter performance at the moment, which is one of the main influences on warmup. There's a blog post here from August about how the compiler is being taught to recognize and optimize bytecode interpreter loops, to get results that normally require handcrafted assembly:
https://medium.com/graalvm/escaping-the-giant-switch-dec20b5...
So as you know already, warmup was historically a weakness of PE based stuff - although in this case the fair comparison is with CPython which never warms up at all - but a fast interpreter will make a big difference. In some benchmarks the GraalPy interpreter is already faster than the CPython interpreter, which is heavily optimized.
I think if the Python community wants an implementation that has a proven JIT and where the remaining work is easily scaled compatibility testing, then there's one available and ready. Whether they're calling into C or Rust or FORTRAN or Java shouldn't make a big difference in principle.
Now, I've met lots of people who think that binding to Rust will always be the fastest thing to do but that underestimates Java significantly, especially once you factor in the fact that GraalPy can optimize out interop overheads.
Consider LLM tokenization. It's one of the most heavily optimized pieces of code on the planet. Yet, toknroll written in Java can beat tiktoken which is written in Rust:
https://github.com/qxoticai/qxotic/tree/main/toknroll
Moreover the Rust code has some unsafe{} blocks as Rust often does, whereas toknroll has no unsafe code anywhere.
So the scripting language communities need to think hard about where they go next, IMHO. DHH already got up and announced at a Rails conference that Ruby is over, you should use Rust for everything. I don't think that's quite true, but it's going to be a meme that with agents you should just pick the fastest language and go. If Python/Ruby/etc want to stay relevant then they need to care a lot more about their performance story. Just being more pleasant to read and write for humans doesn't cut it anymore. CPython is clearly not going to happen in this regard, so alternative implementations need to start being taken a lot more seriously than they were in the past.
They are Python libraries. Don't make the mistake of screwing up the cause and effect here. C, Rust etc - those are the replaceable parts of the chain. There is an official C++ pytorch library and virtually nobody uses it for ML Research. People want to code in python and build those libraries so they can.
PyTorch in C++ naturally gets little use, because most people don't want to use C++, and although possible, e.g. CINT, very few care about C++ REPLs.
Also these libraries only exist because Python cannot deliver on its own, like Lisp or Scheme, and needs such libraries in first place.
Even BASIC has better options to do machine code and system programming than Python, starting with the original Dartmouth BASIC.
They are python libraries because people reached for those languages so they could continue work in python. They are there to serve python and nothing else. It's the same with Rust. It's by no means a C++ thing.
EDIT: Not saying this is always the case. Qt>Pyside is an example where it's both technically and functionally bindings, but the vast majority of the time they are serving python. Some don't even have a native library to speak of.
Otherwise they are bindings to native libraries.
We can keep discussing in circles.
So if you can't call/use the 'library' from the 'native' language, it's still a library? Whatever floats your boat. Lol.
Otherwise it’s just a sparkling FFI.
Python is a mature production language with a lot of users. Even before it was huge - like 20 years ago when I was in college - getting a small backwards compatible API change into a python extension module (gnu readline in my case) required convincing GvR personally that the API change was necessary.
Every JIT attempt I've seen for python just tries to cut too deep in the first pass, and scope themselves too large.
I believe any successful attempt at optimizing python performance from JIT will FIRST start with a 100% semantically compatible hidden type model for optimizing object property access and method dispatch.
The single biggest performance delta in most dynamic language jits come from that, not from optimizing guards off of bytecode operations or from compiling to machine code. It's from converting hashtable lookups into constant-time guard + offset lookups.
The hidden type model is also a hard nut to crack. It needs to perserve dict semantics which have very dynamic behaviour via the various `__*__` methods, and handle exotics scope objects since scoped name lookups are the most common ones which have their own exotic behaviours (I remember python providing some ability for callee frames to inspect caller scopes in some limited ways).
Today there are far more people using it, and everyone has a different perspective on what it is. No longer is it a niche glue language, now it's a backbone of AI technology at major tech firms, so it's understandable they'd like it to be faster on its own.
Probably also during the 2 to 3 migration, but that goal appears to have now been abandoned.
I wonder how the authors of other implementations like pypy will be able to keep up as the reference implementation becomes more and more complex.
The problem was he exposed too much of the implementation into the language itself, destroying its ability to be a simple reference implementation that others could re-implement.
The python documentation has historically also not been especially clear at distinguishing between what the language guarantees, vs what is just implementation details of the cPython implementation. So even if libraries wanted to try to avoid depending on implementation specifics, it isn't always clear what those are.
The net result is that for the end user python the language is effectively: whatever cpython does. This really, really discourages creating alternate implementations, as the only way to be an alternative implementation with good compatibility is to be a living fork of cpython, constantly merging in all the changes from it, while also maintaining your improvements.
Last release on Oct 14. Beta support for 3.12. It's not that bad really and 1/10 of the people who complain about it did something then the situation would be better.
https://peps.python.org/pep-3000/ - PEP 3000 says it will be a derived from the Python 2 codebase and not a complete rewrite (referencing Joel Spolsky's views on complete rewrites and Netscape Navigator).
https://peps.python.org/pep-3099/ - PEP 3099 is changes Guido van Rossum rejected from Python 3 (including := as a binding operator), parallel iteration, programmable syntax, parser going beyond LL(1), changing the for/else construct.
https://peps.python.org/pep-3100/ - PEP 3100 is things he set as goals for it, things like all strings are Unicode and byte arrays are a separate type, optional type annotations, 'exec' and 'print' change from statements to functions, None/True/False become keywords, old-style classes are gone, changing inequality comparison from <> to != , require C99, drop BeOS support, drop old standard library modules, and more.
actually, more changes than I remember.
Python already does it in a lot of cases, see opcode specializations like LOAD_ATTR_INSTANCE_VALUE, LOAD_ATTR_SLOT.
More digging led to this: https://peps.python.org/pep-0659/ which seems to be what the effort is organized around?
Python's object semantics are broadly similar to Javascript, bar the prototypical inheritance chain, but with the extra complexity of potentially needing to handle dynamic `__*attr__` overrides. Optimizing for this would require a tree of hidden map objects much like JS JIT engines use. I dug around the codebase a while back looking for something like that but didn't find it. `OrderedDict` seemed promising but after more investigation that turned out to just be adding property ordering to dictionaries.
The thing is, you can build that entire hidden type structure out, AND optimize against it, without ever having to mess with things like bytecode replacement. Once you have the hidden type model, you can modify the property lookup algorithm in the VM itself (as in cpython c code) to use some annotation structure hanging off of the bytecode to look up inline caches of type-guarded fastpaths. It would be an optimized fastpath that integrated directly into the C code that interprets the GETATTR opcode.
I don’t really think that’s true or even that relevant , at least in context of classes (especially with slots - the class, by definition, _is_ the “hidden map”) and not generic dicts.
> The thing is, you can build that entire hidden type structure out, AND optimize against it, without ever having to mess with things like bytecode replacement.
Pretty sure the motivation was exactly the opposite - that a simple “if a LOAD_ATTR encounters the same class with slots N times in a row, swap it out for LOAD_ATTR_SLOT with inline cached slot index, and swap back to generic one if something changes” is way easier (both to implement and reason about) than any alternative that’d interact with the object model. (Also it means you skip having to branch between a slow and fast path at all)
> you can modify the property lookup algorithm in the VM itself (as in cpython c code) to use some annotation structure hanging off of the bytecode to look up inline caches of type-guarded fastpaths. It would be an optimized fastpath that integrated directly into the C code that interprets the GETATTR opcode.
Sorry, that sounds a bit vague and not too different from what CPython already is doing.
> Sorry, that sounds a bit vague and not too different from what CPython already is doing.
With slots being available, I can see where I was misled. I went looking for a type structure that modeled the dynamic properties on an object which would be used as a typeguard, but the class itself functions as that if you're restricting the optimization to slotted properties.
Appreciate the clarfication!
I suppose the various JIT projects are trying to come at that from different angles incrementally, but that's a genuinely hard problem as you don't see real benefits until multiple complex pieces of infrastructure are in place and with good coverage over the language.
Anyway, my hot take was misplaced.
It would have been more honest to just cut the feature instead of haranguing a developer until they withdrew it.
To be clear, such a request doesn't even mean that someone is consciously attempting to kill the project (although in the corporate world, ime it probably does). It just means that something went critically wrong. In this case, it appears like python's development process really required a PEP for this sort of work, especially since it was happening on the main branch.
>> For example, rather than proposing one single concrete JIT implementation, it may make more sense for the PEP to describe a JIT infrastructure that can support multiple implementation strategies.
Are the JIT developers the same people who were working on Faster CPython at Microsoft? No surprise that they don't have the same amount of energy to dedicate to the JIT now that it's gone from a full-time job to a hobby project.
I wouldn't be surprised if they all went nodejs instead :/
His current focus seems to be speeding up GC and free threading.
The static + AOT approach has a lot of promise now that type annotations are widely adopted, predictable behavior and good performance on code that is mostly idiomatic Python seem like a good alternative in many situations to the weight of Rust.
What about the other major change to Python's implementation: free threading? Has it lived up to expectations? I assume it's not also in doubt, despite Guido recently saying "Personally I wish we’d stuck with the GIL"? [0]
[0] https://discuss.python.org/t/pep-805-safe-parallel-python/10...
It boggles my mind that it apparently is the case that such a popular language as Python isn't able to get the resources to do this.
> We want the PEP to be clear that it’s ultimately the JIT team’s responsibility to find solutions for these packages and tools, and how to deal with situations where a tool can’t be supported or made to work
Is it just me or is this absurdly impossible, to the point of willfully killing the project? Committing a team of brilliant compiler engineers to doing free consulting ad infinitum for every bit of tooling in the world, however hacked-together?
The alternative would be to have the bravery to say: this is the way forward to keep this language relevant into the future, and as a steering committee we collectively own that some tooling may be out of date. Instead, this is a signal that Python is going to rest on its laurels for a very long time.
Where do we go from here? Microsoft did not continue to finance the Faster CPython project and the current speed gains are good, but not what we hoped.
The problem is that Python just isn't going to go fast. There are some languages that may have had more resources poured into them to make them go faster in absolute terms, like perhaps Java. But I don't think there's a single language that has had so many ways of trying to make it go faster given so much effort over so many decades. It's really time to call it... if you have Python code and it's a performance problem, if one of the many many many different solutions aren't adequate for some reason, Python is not going to get any faster than it is today and you're going to have to move off of it.
If you need high performance code, Python is simply not the right answer and you're going to have to skill up. Or accept what you have, which you'll have to decide if that's a viable option. If it's fast enough I don't advocate switching casually. But if it isn't, there isn't a realistic prospect that you're going to wake up in a year or two to a 30x-faster Python. I wasn't terribly convinced by that claim back in the early 2000s, but it hadn't really been tested yet. It has been tested now.
> RustPython is in development, and while the interpreter certainly can be used in interesting use cases like running Python in WASM and embedding into a Rust project, do note that RustPython is not totally production-ready.
Second, no, it isn't. Pypy is really fast compared to Python. "Compared to Python" is just about the lowest bar there is, though. In absolute terms it is still only a medium-fast language runtime, and it comes with a lot of costs and tradeoffs.
That's the problem with Python. Lots of ways to take a very slow language and make it a medium-fast language with substantial tradeoffs, or make it a fast language with enormous tradeoffs. If you can handle the tradeoffs, fine. But if you can't you just need to switch to a language that is fast without anywhere near as many tradeoffs.
But yeah, clearly nobody's willing to rip off the bandaid, so that's not happening.
You don't have to throw away the original Python codebase.
You can have:
1. An implementation in slow language (i. e. Python) that you maintain manually or semi-manually (with minimal AI assistance) and understand well.
2. A fast implementation that is fully generated and maintained by AI, using slow implementation (1) as a reference.
You can, for example, run two "adversarial" agents, one writes tests that compare output of (1) and (2) and tries to find a discrepancy, the other tries to fix (2) so that its output never differs from (1).
This way, you can be reasonably confident that (2) does exactly the same thing as (1), despite being a blackbox that you don't understand. While (1) remains a reference implementation that you understand well and can work with.
F# is on the softest side of that, but letting the compiler tell the AI "nope, here's why" seems like a huge win in saving cycles and time.
The downside at first was simply, "how many examples of F# are there to learn from", and early on it showed, but I think that's been long overcome.
And F# is obviously NOT the best example since it's both "soft" on functional purity and niche. I imagine something like haskell is probably doing even better, although I've yet to check that at all (not that I can code haskell but I haven't looked at anecdotes or anything).
I realize this doesn't really help anyone who's grown to love python, and there's surely no perfect language, but just like how the web catapulted javascript, I see a future where the answer isn't "use AI to make python better" so much as "what already works super well with AI, and does it ease the user adoption"
AI needs formal verification, not a borrow checker.
Clearly the community doesn't want PyPy, GraalPy, or even a CPython JIT.
Implementation effort has never been what has held these efforts back. LLM helps with squirting out code, but the hard tasks are benchmarking, profiling, community engagement, knowledge management, and dealing with the hard questions such as whether the change is actually worth the additional complexity.
These concerns won't go away even if we give up on human developers being able to grok the code and instead completely relying on LLMs for coding.
V8 made JavaScript fast in 2008, soon that will be 20 years ago.
Perhaps I live in a bubble, but so much of the production Python code I see is fully type-hinted and makes used of an aggressive linter & type checker to the point where it might as well be written in Go/C#/Java/TypeScript so that it can actually take advantage of the performance gains instead of being loaded into a runtime that turns everything into heap-allocated PyObject pointers.
Python interpreter is known to leak its implementation (__class_ modification after creation, monkey patching with __getattr__, sys._getframe, eval/exec, deterministic call to __del__, no proper python API for libraries, etc.)
You have to plug all of those holes before having a chance to get a decent JIT. Otherwise it will be just another JIT attempt, like Psyco, Unladen Swallow, Pyston.
V8 predates strict mode, and handles all the exec/eval, monkey patching and object system abuse you can do in JavaScript just fine. Common Lisp is one of the most dynamic languages in the world and allows you to do ridiculous things in terms of meta-programming and essentially building domain-specific languages into your program, yet it too manages to have ahead-of-time compiled implementations that are very performant. Also PyPy exists.
There are definitely some technical aspects that make it hard to make Python fast, but they are not insurmountable. The reason CPython is slow today is simply because performance has not historically been prioritized by its developers.
If I think about where I see python being used though, this kind of makes sense, the two big use cases are:
- Web APIs and backends, where for the most part either the network or the database is the bottleneck
- Data science work, where for the most part, work exists in libraries like polars, pytorch etc that ship the main work out to other languages.
So from that perspective, although we'd all like python do be faster, it's speed isn't the bottleneck in the use cases where its popular (that's probably also the reason why we don't see Python getting used in other speed critical places).
That's a really different story to JavaScript, which doesn't have a handy CFFI or otherwise you can just reach out to. There are a lot of cases where JavaScript speed improving would speed up the reactivity of a web app, even now.
I'm not arguing against a faster CPython - I'd love one! But I think there's a clear reason why it hasn't been prioritised historically in the way JavaScript's performance has.
If you can't do that, then, forking is the right strategy.
It does not suggest one strategy is better than the other.
Plenty of `grown ups` disagree; It's natural.
If JIT is different enough in maintenance, security best practices, and consumer use, we don’t have two compilers, we have two different languages.
And that’s the crux of it.
Ruby is on its third (I think?) JIT now? I'm a little surprised, given Ruby's reputation as a wacky "anything goes" language with a very complex grammar, and it makes me wonder why that is? Does it have to do with the design of the runtime itself more than the language semantics? Or is it more incidental, e.g. many popular ibs are C wrappers, already generates bytecode files, and so on.
If the Python steering committee isn't willing to do the same, they'll probably won't get a working JIT any time soon. Developing a JIT externally, or even from a branch, is just too hard.
Why had Python to succeed to Lisp in the AI wave? (rhetoric question)
Since PyPy and GraalPy will never get the love they deserve, I guess there is hope that Julia, Mojo, and the whole Rewrite in Go/Rust keep pushing along then.
In my view, the right course of action is to specify an official strictly typed subset of the language with compilation and static analysis as the justification, then implement both JIT and AOT on top of that. Legacy code can be rewritten or thrown out in phases, C extensions can be ported.
Of course, any such proposal would invite monumental amounts of bikeshedding, which is why it wouldn't get far, much like the JIT proposal.
This is a great idea for popular dynamic languages with existing users and codebases, particularly the focus on "subset" to limit the scope and emphasizing that full backward compatibility is not a goal; and allowing for gradual, incremental adoption. That makes it a lot more practical to implement and adopt.
I think we'll see more of this. Recent examples include strictly typed TypeScript subset designed for ahead-of-time compilation.
Python in itself is poorly suited to ever become performant as it lacks the types required. I imagine that causes some issues for effective jit-ing too.
They had a chance to fix this when adding type annotations, but instead of making it "optional/gradual typing" they opted to make it "documentation or linting support", which is obviously the wrong choice, but there were cultural reasons
If it helps python specifically, given that the types themselves are pretty dynamic, who knows. A python integer doesn't map well to a native integer etc. Python really isn't designed for performance, and retrofitting it is hard.
To me, goals without plans behind them aren't really goals at all
As usual, though, I might just be biased by default towards defending the Python aristocracy; they seem to pretty much always be on the right side of history, even in the little ways!
Python is my favorite language, but I'm honestly tired of having to rewrite performance bottlenecks in Cython or Rust all the time. When the Faster CPython project started with Microsoft's backing, I was really excited. Mark Shannon had that plan promising huge speedups across a few cycles, and while that clearly turned out to be much harder than anticipated, the team still kept pushing forward to deliver a real JIT.
Seeing all of that get bogged down in committee bureaucracy now is just frustrating.
I have a lot of respect for the core devs and I know maintaining CPython, and it is a time donation job. It isn't easy. But I feel that over-caution and red tape ends up discouraging serious contributions. They are setting an unreasonably high bar for something that would obviously help the entire ecosystem.
Just think about the sheer amount of electricity and compute power being wasted every single day running inefficient Python across millions of machines worldwide.
Python really deserves a proper JIT. I genuinely hope they reconsider this.
PyPy lacks upstream interest, looked like it could have a shot after many years of development but recently NumPy removed support because they just couldn’t fathom how a non-dead project could be a couple of versions behind CPython.
Now the new JIT is dying because of what seems to be a lack of upstream interest. (The things TFA describes as being required are not unreasonable, but not realistic for a necessarily speculative long-running project without wild amounts of committed funding.)
I’m not optimistic.
IIRC Unladen Swallow was, despite the extreme hype, nothing more than the internship project of a couple of students. It died because their internship was over.
Otherwise you're right, though. Lots of CPython alternatives suffer from lack of upstream interest - the Python language designers don't seem very interested in anything other than CPython. In particular, they appear to only consider CPython when adding features to the language. Two examples:
- Requiring `dict`s to be ordered because that's how they work in CPython, without considering how they work in other implementations
- Adding pattern matching syntax that's suitable for CPython's PEG parser, even though it's not LL(1) so can't be parsed by, say, MicroPython
That's what's kept the language and ecosystem improving and from becoming a hot mess. If something isn't properly specced then it really shouldn't be in prod. They allowed the experiment in the first place so it'd be easier to gather sufficient data to guide said speccing, but the ball was dropped by the proposers and so the experiment was an ultimate failure (unlike free threading) and prod is now being appropriately cleaned up.
Every single time I remember the first AI wave was done with Lisp and Prolog, both with tooling that supports machine code generation since about 50 years.
I actually expected something good to come out of this, my mistake for trusting Python "engineers". Never again.
In the remote eventuality that it should actually work and be maintained up to date and secure, I'm sure it will acquire users.
Wrote a parser with a rust compatible pattern matching syntax here:
https://github.com/py2many/spy-ast
Should help both JIT and static translation.
AI literally turned Python from this silly esolang that PhDs seem to love into the Lingua Franca of Computing.
All LLM tools are built Python-first and all LLMs are trained Python-first.
If there is anything you can believe in beyond gravity and taxes is that an LLM's first tokens on a new shell will be py thon 3.
Source?
for me it's over for python
Under these premises is very difficult that something not implemented correctly is really exploitable, and moreover it's simple to verify the security of the whole thing.
Opening a JIT, that would mean mapping memory pages as RWX, would open the door to all soft of exploits that now are not really possible.
Can you name some of these JS engine vulnerabilities? And the PHP one had nothing to do with JIT.
For JS, your machine running untrusted JS code is the intended use case. In this scenario the JS is sandboxed in whatever JS engine (V8 etc) and does not have access to "dangerous" methods, and the main use case for a JIT vuln is to escape this sandbox to execute code on the host. This applies to regular internet browsing (and visiting potentially malicious sites) and also, e.g., using the built-in browser in a locked down device (such as game consoles) to gain arbitrary code execution.
For Python, this security boundary doesn't exist. I can't really think of a scenario where you would be running potentially malicious Python code in CPython that would need a JIT vuln to escape whatever sandbox it is in instead of, say, calling the functions in Python directly (or using the C FFI to do whatever). I'm sure that in some niche use cases, this scenario might exist, but to me, it seems like the potential attack surface is fairly minimal.
EDIT: Also, I'm pretty sure that Python's copy-and-patch JIT writes code to a writable mapping and then mprotects it to W^X? I don't think it leaves RWX pages lying around at all.
JS has a completely different threat model. And PHP has just been a basketcase from inception.
I'll get what I want eventually but I might be retired by then.